https://github.com/gagneurlab/manuscript_avsec_bioinformatics_2017
Code for Avsec et al, Bioinformatics 2017
https://github.com/gagneurlab/manuscript_avsec_bioinformatics_2017
Last synced: 2 months ago
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Code for Avsec et al, Bioinformatics 2017
- Host: GitHub
- URL: https://github.com/gagneurlab/manuscript_avsec_bioinformatics_2017
- Owner: gagneurlab
- Created: 2017-11-16T13:49:02.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-05T13:12:08.000Z (over 6 years ago)
- Last Synced: 2025-01-25T04:29:21.381Z (4 months ago)
- Language: Jupyter Notebook
- Size: 87.4 MB
- Stars: 3
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Manuscript_Avsec_Bioinformatics_2017
Code for [Avsec et al, Bioinformatics 2017](https://doi.org/10.1093/bioinformatics/btx727) (Bioarxiv preprint [Avsec et al, Bioarxiv 2017](https://doi.org/10.1101/165183)).
## Installing requirements
### Python
Note, use python>3.5 from anaconda and use virtual environments to not interfere with your default python environment.
```{python}
pip install -r python3_requirements.txt
```
### R
Start R using: `R --vanilla`.
Install CRAN R packages:
```{r}
install.packages(readLines("r_packages.txt"))
```
Install Bioconductor packages:
```{r}
source("https://bioconductor.org/biocLite.R")
sapply(readLines("r_bioc_packages.txt"), biocLite)
```
## General repository organization notes
In each experiment folder training predictive models, the main files are:
- readme.md - contains further instructions
- data.py - contains a `data()` function returning a tuple of train and test-set arrays
- model.py - contains a `model()` function returning a Keras model
- train.py - runs model training and hyper-parameter optimization
R should be started from the repository root.
All data are located either in `Data` (smaller clip data) or in `data/` (everything else).
## Download the data
To download the rest of the data not contained in the repository, run:
```bash
wget https://i12g-gagneurweb.in.tum.de/public/paper/Avsec_Bioinformatics_2017/data.tar.gz
tar xvfz data.tar.gz
```
`data/` includes intermediary results, trained models, as well as model training/test datasets.
## Support
Let me know if you have any problems by creating an issue or sending me an email to avsec-at-in.tum.de.